Analyze Survey Data using Principal Component Analysis

4.1
별점
32개의 평가
제공자:
Coursera Project Network
학습자는 이 무료 안내 프로젝트에서 다음을 수행하게 됩니다.

Understand the fundamentals of Principal Component Analysis (PCA) and identify opportunities to combine variables.

Conduct correlation testing with various sets of variables in Google Sheets.

Combine highly correlated variables, visualize the data, and consider next steps in Google Sheets.

인터뷰에서 이 안내형 체험 보여주기

Clock2 hours
Advanced고등
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

Survey data sets are often deceptively complex because surveys collect a wide variety of data covering a wide variety of topics and experiences. To further the complexity of survey data, the respondents answering the questions come from a wide variety of backgrounds and stages in their customer journey. It is reasonable that it would be a challenge to boil down survey data into actionable insights because it can be deceptively complex. With large sets of data, Principal Component Analysis or PCA is a useful tool that reduces and transforms variables to a leaner form that allows for a speedier analysis. In this project you will gain hands-on experience with the principles of Principal Component Analysis using survey data. To do this you will work in the free-to-use spreadsheet software Google Sheets. By the end of this project, you will be able to confidently apply Principal Component Analysis concepts to transform large sets of variables into a leaner set of data that still contains the most relevant information. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

요구 사항

Familiarity with spreadsheet software, factor analysis, and correlation testing. "Design a Factor Analysis Using Survey Data" is recommended.

개발할 기술

Survey MethodologyMining InsightsBusiness InsightsData AnalysisPrincipal Component Analysis (PCA)

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Review the fundamentals of Principal Component Analysis (PCA) and combining variables.

  2. Identify use cases for PCA and refine variable selection for the project.

  3. Access Google Sheets, import survey data, and examine variables that are likely correlated.

  4. Identify variables of interest and conduct a correlation test.

  5. Compare results and review the process of correlation testing.

  6. Combine highly correlated variables, create a visualization, and consider next steps.

  7. Access the ClustVis webtool for visualizing clustering and multivariate data.

  8. Build a PCA model with Heart data and run a Principal Component Analysis

  9. Compare results and review PCA with multivariate data from multiple sources and interpret the findings in ClustVis.

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

검토

ANALYZE SURVEY DATA USING PRINCIPAL COMPONENT ANALYSIS의 최상위 리뷰

모든 리뷰 보기

자주 묻는 질문

자주 묻는 질문

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